The influence of modality and transparency on trust in human-robot interaction

Sanders, Tracy; Wixon, Tarita; Schafer, K. Elizabeth; Chen, Jessie Y. C.; Hancock, Peter A.

doi:10.1109/cogsima.2014.6816556

Cited by 52 publications

(31 citation statements)

References 12 publications

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“…Explanations improve usability and let the users understand what is happening, building the users' trust and generating calibrated expectations about the system's capabilities (Westlund and Breazeal, 2016 ). If systems can explain their reasoning, they should be easily understood by their users, and humans are more likely to trust systems that they understand (Sanders et al, 2014 ; Sheh, 2017 ; Fischer et al, 2018 ; Lewis et al, 2018 ).…”

Section: Transparency As Explainabilitymentioning

confidence: 99%

System Transparency in Shared Autonomy: A Mini Review

Alonso

Puente

2018

Front. Neurorobot.

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What does transparency mean in a shared autonomy framework? Different ways of understanding system transparency in human-robot interaction can be found in the state of the art. In one of the most common interpretations of the term, transparency is the observability and predictability of the system behavior, the understanding of what the system is doing, why, and what it will do next. Since the main methods to improve this kind of transparency are based on interface design and training, transparency is usually considered a property of such interfaces, while natural language explanations are a popular way to achieve transparent interfaces. Mechanical transparency is the robot capacity to follow human movements without human-perceptible resistive forces. Transparency improves system performance, helping reduce human errors, and builds trust in the system. One of the principles of user-centered design is to keep the user aware of the state of the system: a transparent design is a user-centered design. This article presents a review of the definitions and methods to improve transparency for applications with different interaction requirements and autonomy degrees, in order to clarify the role of transparency in shared autonomy, as well as to identify research gaps and potential future developments.

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Section: Transparency As Explainabilitymentioning

confidence: 99%

System Transparency in Shared Autonomy: A Mini Review

Alonso

Puente

2018

Front. Neurorobot.

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“…In Experiment 1, we are examining the effects of varying levels of information (LOI) provided to a participant by a robotic teammate upon the participant's trust and workload, as well as the influence of the modality of communication (auditory, graphic, or video). Partial results of this work may be found in Sanders et al [68]. In Experiment 2, which is also ongoing, we are again varying LOI and modality and measuring trust workload, but this experiment also includes a malfunction condition wherein the communication between the participant and the robot takes place during incidents of malfunction.…”

Section: Trust and Transparency In Hrimentioning

confidence: 91%

Determinants of system transparency and its influence on trust in and reliance on unmanned robotic systems

et al. 2014

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Increasingly autonomous robotic systems are expected to play a vital role in aiding humans in complex and dangerous environments. It is unlikely, however, that such systems will be able to consistently operate with perfect reliability. Even less than 100% reliable systems can provide a significant benefit to humans, but this benefit will depend on a human operator's ability to understand a robot's behaviors and states. The notion of system transparency is examined as a vital aspect of robotic design, for maintaining humans' trust in and reliance on increasingly automated platforms. System transparency is described as the degree to which a system's action, or the intention of an action, is apparent to human operators and/or observers. While the physical designs of robotic systems have been demonstrated to greatly influence humans' impressions of robots, determinants of transparency between humans and robots are not solely robot-centric. Our approach considers transparency as emergent property of the human-robot system. In this paper, we present insights from our interdisciplinary efforts to improve the transparency of teams made up of humans and unmanned robots. These nearfuturistic teams are those in which robot agents will autonomously collaborate with humans to achieve task goals. This paper demonstrates how factors such as human-robot communication and human mental models regarding robots impact a human's ability to recognize the actions or states of an automated system. Furthermore, we will discuss the implications of system transparency on other critical HRI factors such as situation awareness, operator workload, and perceptions of trust.

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“…The robot's action, communication, and transparency not only can increase task performance according to Lakhmani et al [17], but operators' well-being in terms of mental workload and situation awareness as noted by Hayes and Shah [18]. Furthermore, Sanders et al [15] propose that a consistent and constant flow of information can have a positive impact on trust. Trust is essential for efficient task completion; according to Wright et al [19], too little trust can result in technology rejection, while too much trust can lead to complacency.…”

Section: Related Workmentioning

confidence: 98%

“…From the user point of view any new technology needs to be accepted by the workforce to be effective. Lack of trust can be caused by a lack of transparency in robot behaviour as shown in the work of Sanders et al and Wortham and Theodorou [15,16]. People are likely to feel more comfortable and confident working with a robot if they know how it behaves and can anticipate what it will do next.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, this User Interface Layer aims to provide an interface able to generate suitable and understandable cues to communicate the status of the co-manipulation task. Although some research suggests that modality of information communication (audio, text, and graphic) does not affect trust and user experience as shown in [15], Selkowitz et al [29] have found that the use of a graphic information display does not significantly increase user workload. In addition to possible positive effects on workload, the graphic modality has the benefit of requiring little experience and training to use [30], and it can be beneficial for people with different information processing abilities due to, for example, dyslexia as discussed in [31].…”

Section: User Interface Layermentioning

confidence: 99%

See 1 more Smart Citation

Dual Arm Co-Manipulation Architecture with Enhanced Human–Robot Communication for Large Part Manipulation

Ibarguren

Eimontaite

Outón

et al. 2020

Sensors

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The emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.

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The influence of modality and transparency on trust in human-robot interaction

Cited by 52 publications

References 12 publications

System Transparency in Shared Autonomy: A Mini Review

System Transparency in Shared Autonomy: A Mini Review

Determinants of system transparency and its influence on trust in and reliance on unmanned robotic systems

Dual Arm Co-Manipulation Architecture with Enhanced Human–Robot Communication for Large Part Manipulation

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